Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

Seyed Azad Nabavi, Alireza Aslani, Martha A. Zaidan, Majid Zandi, Sahar Mohammadi and Naser Hossein Motlagh
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Seyed Azad Nabavi: Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran
Alireza Aslani: Department of Renewable Energy and Environment, University of Tehran, Tehran 14174-66191, Iran
Martha A. Zaidan: Institute for Atmospheric and Earth System Research (INAR)/Physics, University of Helsinki, FI-00014 Helsinki, Finland
Majid Zandi: Faculty of Mechanical and Energy Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran
Sahar Mohammadi: Department of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran 16589-53571, Iran
Naser Hossein Motlagh: Department of Computer Science, University of Helsinki, FI-00014 Helsinki, Finland

Energies, 2020, vol. 13, issue 19, 1-22

Abstract: Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

Keywords: energy modeling; residential and commercial sectors; multiple linear regression; logarithmic multi-linear regression; artificial neural network; NARX; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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